In [1]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import os
%matplotlib inline
import warnings
warnings.filterwarnings("ignore")
In [3]:
df=pd.read_csv("adult.data",header=None)
In [4]:
names=["age","workclass","fnlwgt","education","education-num","marital-status","occupation","relationship","race","sex","capital-gain","capital-loss","hours-per-week","native-country","class"]
In [5]:
df.columns=names
In [6]:
df.head()
Out[6]:
age workclass fnlwgt education education-num marital-status occupation relationship race sex capital-gain capital-loss hours-per-week native-country class
0 39 State-gov 77516 Bachelors 13 Never-married Adm-clerical Not-in-family White Male 2174 0 40 United-States <=50K
1 50 Self-emp-not-inc 83311 Bachelors 13 Married-civ-spouse Exec-managerial Husband White Male 0 0 13 United-States <=50K
2 38 Private 215646 HS-grad 9 Divorced Handlers-cleaners Not-in-family White Male 0 0 40 United-States <=50K
3 53 Private 234721 11th 7 Married-civ-spouse Handlers-cleaners Husband Black Male 0 0 40 United-States <=50K
4 28 Private 338409 Bachelors 13 Married-civ-spouse Prof-specialty Wife Black Female 0 0 40 Cuba <=50K
In [7]:
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 32561 entries, 0 to 32560
Data columns (total 15 columns):
 #   Column          Non-Null Count  Dtype 
---  ------          --------------  ----- 
 0   age             32561 non-null  int64 
 1   workclass       32561 non-null  object
 2   fnlwgt          32561 non-null  int64 
 3   education       32561 non-null  object
 4   education-num   32561 non-null  int64 
 5   marital-status  32561 non-null  object
 6   occupation      32561 non-null  object
 7   relationship    32561 non-null  object
 8   race            32561 non-null  object
 9   sex             32561 non-null  object
 10  capital-gain    32561 non-null  int64 
 11  capital-loss    32561 non-null  int64 
 12  hours-per-week  32561 non-null  int64 
 13  native-country  32561 non-null  object
 14  class           32561 non-null  object
dtypes: int64(6), object(9)
memory usage: 3.7+ MB
In [8]:
df.describe(include="all")
Out[8]:
age workclass fnlwgt education education-num marital-status occupation relationship race sex capital-gain capital-loss hours-per-week native-country class
count 32561.000000 32561 3.256100e+04 32561 32561.000000 32561 32561 32561 32561 32561 32561.000000 32561.000000 32561.000000 32561 32561
unique NaN 9 NaN 16 NaN 7 15 6 5 2 NaN NaN NaN 42 2
top NaN Private NaN HS-grad NaN Married-civ-spouse Prof-specialty Husband White Male NaN NaN NaN United-States <=50K
freq NaN 22696 NaN 10501 NaN 14976 4140 13193 27816 21790 NaN NaN NaN 29170 24720
mean 38.581647 NaN 1.897784e+05 NaN 10.080679 NaN NaN NaN NaN NaN 1077.648844 87.303830 40.437456 NaN NaN
std 13.640433 NaN 1.055500e+05 NaN 2.572720 NaN NaN NaN NaN NaN 7385.292085 402.960219 12.347429 NaN NaN
min 17.000000 NaN 1.228500e+04 NaN 1.000000 NaN NaN NaN NaN NaN 0.000000 0.000000 1.000000 NaN NaN
25% 28.000000 NaN 1.178270e+05 NaN 9.000000 NaN NaN NaN NaN NaN 0.000000 0.000000 40.000000 NaN NaN
50% 37.000000 NaN 1.783560e+05 NaN 10.000000 NaN NaN NaN NaN NaN 0.000000 0.000000 40.000000 NaN NaN
75% 48.000000 NaN 2.370510e+05 NaN 12.000000 NaN NaN NaN NaN NaN 0.000000 0.000000 45.000000 NaN NaN
max 90.000000 NaN 1.484705e+06 NaN 16.000000 NaN NaN NaN NaN NaN 99999.000000 4356.000000 99.000000 NaN NaN
In [9]:
df[df.duplicated()]
Out[9]:
age workclass fnlwgt education education-num marital-status occupation relationship race sex capital-gain capital-loss hours-per-week native-country class
4881 25 Private 308144 Bachelors 13 Never-married Craft-repair Not-in-family White Male 0 0 40 Mexico <=50K
5104 90 Private 52386 Some-college 10 Never-married Other-service Not-in-family Asian-Pac-Islander Male 0 0 35 United-States <=50K
9171 21 Private 250051 Some-college 10 Never-married Prof-specialty Own-child White Female 0 0 10 United-States <=50K
11631 20 Private 107658 Some-college 10 Never-married Tech-support Not-in-family White Female 0 0 10 United-States <=50K
13084 25 Private 195994 1st-4th 2 Never-married Priv-house-serv Not-in-family White Female 0 0 40 Guatemala <=50K
15059 21 Private 243368 Preschool 1 Never-married Farming-fishing Not-in-family White Male 0 0 50 Mexico <=50K
17040 46 Private 173243 HS-grad 9 Married-civ-spouse Craft-repair Husband White Male 0 0 40 United-States <=50K
18555 30 Private 144593 HS-grad 9 Never-married Other-service Not-in-family Black Male 0 0 40 ? <=50K
18698 19 Private 97261 HS-grad 9 Never-married Farming-fishing Not-in-family White Male 0 0 40 United-States <=50K
21318 19 Private 138153 Some-college 10 Never-married Adm-clerical Own-child White Female 0 0 10 United-States <=50K
21490 19 Private 146679 Some-college 10 Never-married Exec-managerial Own-child Black Male 0 0 30 United-States <=50K
21875 49 Private 31267 7th-8th 4 Married-civ-spouse Craft-repair Husband White Male 0 0 40 United-States <=50K
22300 25 Private 195994 1st-4th 2 Never-married Priv-house-serv Not-in-family White Female 0 0 40 Guatemala <=50K
22367 44 Private 367749 Bachelors 13 Never-married Prof-specialty Not-in-family White Female 0 0 45 Mexico <=50K
22494 49 Self-emp-not-inc 43479 Some-college 10 Married-civ-spouse Craft-repair Husband White Male 0 0 40 United-States <=50K
25872 23 Private 240137 5th-6th 3 Never-married Handlers-cleaners Not-in-family White Male 0 0 55 Mexico <=50K
26313 28 Private 274679 Masters 14 Never-married Prof-specialty Not-in-family White Male 0 0 50 United-States <=50K
28230 27 Private 255582 HS-grad 9 Never-married Machine-op-inspct Not-in-family White Female 0 0 40 United-States <=50K
28522 42 Private 204235 Some-college 10 Married-civ-spouse Prof-specialty Husband White Male 0 0 40 United-States >50K
28846 39 Private 30916 HS-grad 9 Married-civ-spouse Craft-repair Husband White Male 0 0 40 United-States <=50K
29157 38 Private 207202 HS-grad 9 Married-civ-spouse Machine-op-inspct Husband White Male 0 0 48 United-States >50K
30845 46 Private 133616 Some-college 10 Divorced Adm-clerical Unmarried White Female 0 0 40 United-States <=50K
31993 19 Private 251579 Some-college 10 Never-married Other-service Own-child White Male 0 0 14 United-States <=50K
32404 35 Private 379959 HS-grad 9 Divorced Other-service Not-in-family White Female 0 0 40 United-States <=50K
In [10]:
df=df.drop_duplicates()
In [11]:
df.shape
Out[11]:
(32537, 15)
In [12]:
df["class"]=df["class"].replace(" <=50K","-1")
In [13]:
df["class"]=df["class"].replace(" >50K","1")
In [14]:
df["class"].unique()
Out[14]:
array(['-1', '1'], dtype=object)
In [15]:
import ydata_profiling
In [16]:
profile=df.profile_report()
In [17]:
profile

Summarize dataset:   0%|          | 0/5 [00:00<?, ?it/s]
Generate report structure:   0%|          | 0/1 [00:00<?, ?it/s]
Render HTML:   0%|          | 0/1 [00:00<?, ?it/s]
Out[17]:
























In [18]:



     


df["class"].value_counts()



     




















    
    
Out[18]:








-1    24698
1      7839
Name: class, dtype: int64
























In [19]:



     


df["class"].value_counts(normalize=True)*100



     




















    
    
Out[19]:








-1    75.907428
1     24.092572
Name: class, dtype: float64
























In [20]:



     


sns.countplot(x=df["class"])
plt.show()



     




















    
    































In [21]:



     


capital_gain=df[df["capital-gain"]>0]



     



















In [22]:



     


plt.figure(figsize=(7,7))
plt.xticks(rotation = 90)
sns.boxplot(x='class',y='capital-gain',data=capital_gain)
plt.show()



     




















    
    































In [23]:



     


capital_loss=df[df["capital-loss"]>0]



     



















In [24]:



     


plt.figure(figsize=(7,7))
plt.xticks(rotation = 90)
sns.boxplot(x='class',y='capital-loss',data=capital_loss)
plt.show()



     




















    
    































In [25]:



     


capital_gain.groupby(["class"])["capital-gain"].count()



     




















    
    
Out[25]:








class
-1    1035
1     1677
Name: capital-gain, dtype: int64
























In [26]:



     


capital_gain["capital-gain"].unique()



     




















    
    
Out[26]:








array([ 2174, 14084,  5178,  5013,  2407, 14344, 15024,  7688, 34095,
        4064,  4386,  7298,  1409,  3674,  1055,  3464,  2050,  2176,
         594, 20051,  6849,  4101,  1111,  8614,  3411,  2597, 25236,
        4650,  9386,  2463,  3103, 10605,  2964,  3325,  2580,  3471,
        4865, 99999,  6514,  1471,  2329,  2105,  2885, 25124, 10520,
        2202,  2961, 27828,  6767,  2228,  1506, 13550,  2635,  5556,
        4787,  3781,  3137,  3818,  3942,   914,   401,  2829,  2977,
        4934,  2062,  2354,  5455, 15020,  1424,  3273, 22040,  4416,
        3908, 10566,   991,  4931,  1086,  7430,  6497,   114,  7896,
        2346,  3418,  3432,  2907,  1151,  2414,  2290, 15831, 41310,
        4508,  2538,  3456,  6418,  1848,  3887,  5721,  9562,  1455,
        2036,  1831, 11678,  2936,  2993,  7443,  6360,  1797,  1173,
        4687,  6723,  2009,  6097,  2653,  1639, 18481,  7978,  2387,
        5060])
























In [27]:



     


cat=[]
for i in capital_gain["capital-gain"]:
    if i<5000:
        cat.append("Low")
    elif i < 10000:
        cat.append("Medium")
    else:
        cat.append("High")



     



















In [28]:



     


capital_gain["capital_class"]=cat



     



















In [29]:



     


sns.countplot(x=capital_gain["capital_class"])



     




















    
    
Out[29]:








<AxesSubplot: xlabel='capital_class', ylabel='count'>








    
    































In [30]:



     


capital_gain_1=capital_gain[capital_gain["class"]=="1"]



     



















In [31]:



     


sns.countplot(x=capital_gain_1["capital_class"])



     




















    
    
Out[31]:








<AxesSubplot: xlabel='capital_class', ylabel='count'>








    
    































In [32]:



     


capital_gain_2=capital_gain[capital_gain["class"]=="-1"]



     



















In [33]:



     


sns.countplot(x=capital_gain_2["capital_class"])



     




















    
    
Out[33]:








<AxesSubplot: xlabel='capital_class', ylabel='count'>








    
    































In [34]:



     


cat=[]
for i in capital_loss["capital-loss"]:
    if i<5000:
        cat.append("Low")
    elif i < 10000:
        cat.append("Medium")
    else:
        cat.append("High")



     



















In [35]:



     


capital_loss["capital_class"]=cat



     



















In [36]:



     


sns.countplot(x=capital_loss["capital_class"])



     




















    
    
Out[36]:








<AxesSubplot: xlabel='capital_class', ylabel='count'>








    
    































In [37]:



     


df["age"].describe()



     




















    
    
Out[37]:








count    32537.000000
mean        38.585549
std         13.637984
min         17.000000
25%         28.000000
50%         37.000000
75%         48.000000
max         90.000000
Name: age, dtype: float64
























In [38]:



     


plt.figure(figsize=(6,6))
sns.boxplot(df["age"])



     




















    
    
Out[38]:








<AxesSubplot: >








    
    































In [39]:



     


sns.histplot(df["age"],kde=True)



     




















    
    
Out[39]:








<AxesSubplot: xlabel='age', ylabel='Count'>








    
    































In [40]:



     


class_1=df[df["class"]=="1"]



     



















In [41]:



     


sns.histplot(class_1["age"],kde=True)



     




















    
    
Out[41]:








<AxesSubplot: xlabel='age', ylabel='Count'>








    
    































In [42]:



     


class_2=df[df["class"]=="-1"]



     



















In [43]:



     


sns.histplot(class_2["age"],kde=True)



     




















    
    
Out[43]:








<AxesSubplot: xlabel='age', ylabel='Count'>








    
    































In [44]:



     


sns.scatterplot(x="age",y="class",data=df)



     




















    
    
Out[44]:








<AxesSubplot: xlabel='age', ylabel='class'>








    
    































In [45]:



     


group=df.groupby(["sex","age"])["hours-per-week"].mean().reset_index()



     



















In [46]:



     


sns.scatterplot(x="age",y="hours-per-week",hue="sex",data=group)



     




















    
    
Out[46]:








<AxesSubplot: xlabel='age', ylabel='hours-per-week'>








    
    































In [47]:



     


capital_loss["capital-loss"].unique()



     




















    
    
Out[47]:








array([2042, 1408, 1902, 1573, 1887, 1719, 1762, 1564, 2179, 1816, 1980,
       1977, 1876, 1340, 2206, 1741, 1485, 2339, 2415, 1380, 1721, 2051,
       2377, 1669, 2352, 1672,  653, 2392, 1504, 2001, 1590, 1651, 1628,
       1848, 1740, 2002, 1579, 2258, 1602,  419, 2547, 2174, 2205, 1726,
       2444, 1138, 2238,  625,  213, 1539,  880, 1668, 1092, 1594, 3004,
       2231, 1844,  810, 2824, 2559, 2057, 1974,  974, 2149, 1825, 1735,
       1258, 2129, 2603, 2282,  323, 4356, 2246, 1617, 1648, 2489, 3770,
       1755, 3683, 2267, 2080, 2457,  155, 3900, 2201, 1944, 2467, 2163,
       2754, 2472, 1411])
























In [48]:



     


df["native-country"]=df["native-country"].str.replace("?","unknown")



     



















In [49]:



     


country=df["native-country"].value_counts()[:10].sort_values(ascending=False).reset_index()



     



















In [50]:



     


col=["country","count"]



     



















In [51]:



     


country.columns=col



     



















In [52]:



     


sns.barplot(x="country",y="count",data=country,palette="hls")
plt.xticks(rotation=45)
plt.show()



     




















    
    































In [53]:



     


df["occupation"].unique()



     




















    
    
Out[53]:








array([' Adm-clerical', ' Exec-managerial', ' Handlers-cleaners',
       ' Prof-specialty', ' Other-service', ' Sales', ' Craft-repair',
       ' Transport-moving', ' Farming-fishing', ' Machine-op-inspct',
       ' Tech-support', ' ?', ' Protective-serv', ' Armed-Forces',
       ' Priv-house-serv'], dtype=object)
























In [54]:



     


df["occupation"].value_counts(normalize=True)*100



     




















    
    
Out[54]:








 Prof-specialty       12.711682
 Craft-repair         12.582598
 Exec-managerial      12.493469
 Adm-clerical         11.580662
 Sales                11.217998
 Other-service        10.114639
 Machine-op-inspct     6.146848
 ?                     5.664321
 Transport-moving      4.908258
 Handlers-cleaners     4.207518
 Farming-fishing       3.048837
 Tech-support          2.849064
 Protective-serv       1.994652
 Priv-house-serv       0.451793
 Armed-Forces          0.027661
Name: occupation, dtype: float64
























In [55]:



     


plt.figure(figsize=(7,7))
sns.countplot(x="occupation",hue="class",data=df,palette="hls")
plt.xticks(rotation=45)
plt.show()



     




















    
    































In [56]:



     


plt.figure(figsize=(10,10))
sns.countplot(x="education",hue="class",data=df,palette="hls")
plt.xticks(rotation=45)
plt.show()



     




















    
    































In [57]:



     


status=df["marital-status"].value_counts(normalize=False).reset_index()



     



















In [58]:



     


plt.figure(figsize=(7,7))
explode = [0.1, 0.1, 0, 0, 0.1,0.1,0.1]
plt.pie(data=status,x="marital-status",labels="index",autopct='%.0f%%',explode=explode)
plt.show()



     




















    
    































In [59]:



     


sns.countplot(x="workclass",data=df,hue="class",palette="hls")
plt.xticks(rotation=45)
plt.show()



     




















    
    































In [60]:



     


sns.countplot(x="relationship",data=df,hue="class",palette="hls")
plt.xticks(rotation=45)
plt.show()



     




















    
    































In [61]:



     


sns.countplot(x="relationship",data=df,palette="hls")
plt.xticks(rotation=45)
plt.show()



     




















    
    































In [62]:



     


sns.countplot(x="race",data=df,palette="hls")
plt.xticks(rotation=45)
plt.show()



     




















    
    































In [63]:



     


sns.countplot(x="race",data=df,hue="class",palette="hls")
plt.xticks(rotation=45)
plt.show()



     




















    
    































In [64]:



     


sns.countplot(x="sex",data=df,palette="hls")
plt.xticks(rotation=45)
plt.show()



     




















    
    































In [65]:



     


sns.countplot(x="sex",data=df,hue="class",palette="hls")
plt.xticks(rotation=45)
plt.show()



     




















    
    































In [66]:



     


male=df[df["sex"]==" Male"]



     



















In [67]:



     


female=df[df["sex"]==" Female"]



     



















In [68]:



     


sns.countplot(x="workclass",data=male,palette="hls")
plt.xticks(rotation=45)
plt.show()



     




















    
    































In [69]:



     


sns.countplot(x="workclass",data=female,palette="hls")
plt.xticks(rotation=45)
plt.show()



     




















    
    































In [70]:



     


sns.countplot(x="occupation",data=male,palette="hls")
plt.xticks(rotation=90)
plt.show()



     




















    
    































In [71]:



     


sns.countplot(x="occupation",data=female,palette="hls")
plt.xticks(rotation=90)
plt.show()



     




















    
    































In [72]:



     


sns.countplot(x="education",data=male,palette="hls")
plt.xticks(rotation=90)
plt.show()



     




















    
    































In [73]:



     


sns.countplot(x="education",data=female,palette="hls")
plt.xticks(rotation=90)
plt.show()



     




















    
    































In [74]:



     


import os
print(os.environ['PATH'])



     




















    
    






/home/shahabas/anaconda3/envs/jidoka_env/bin:/home/shahabas/anaconda3/bin:/home/shahabas/anaconda3/condabin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin
























In [78]:



     


!export PATH=/Library/TeX/texbin:$PATH



     



















In [ ]: